Review Paper: AI-Driven Plant Disease Diagnosis – A Deep Learning Approach in Precision Agriculture

• Author(s): Mansi Bapu Zanje ; Sneha Balu Shirke ; Ishwari Sanjay Yadav ; Prof. B. B. Deshmukh
• Paper ID: 1708968
• Page: 241-245
• Published Date: 09-06-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 12 June-2025

Abstract

Plant diseases have historically threatened the bounty of the earth, jeopardizing global food security and farmers’ livelihoods. Traditional detection methods — rooted in manual labor and laboratory analysis — remain time-consuming, costly, and inadequate for large-scale farming. As the sun of modern technology rises, artificial intelligence (AI) offers a radiant path forward. Deep learning, a branch of AI, has become the harbinger of precision agriculture, automating plant disease diagnosis with speed and uncanny accuracy. Convolutional Neural Networks (CNNs) process intricate images of diseased leaves, learning to detect the faintest signs of infection. Through transfer learning, AI models build upon existing knowledge, adapting to new plant varieties and disease types with nimble precision. Image processing and data augmentation bolster model performance, overcoming the hurdles of varied environments and data scarcity. This marriage of tradition and innovation empowers farmers to make data-driven decisions, safeguarding their harvests and minimizing pesticide use. Despite these advancements, challenges persist: inconsistent environmental conditions, limited high-quality datasets, and computational constraints in resource-poor settings. Real-world deployment demands lightweight models, accessible interfaces, and collaborations across disciplines. As deep learning interweaves with IoT and edge computing, the promise of real-time, farm-ready diagnosis draws closer. In this paper, we illuminate the journey of AI-driven plant disease diagnosis —its triumphs, its trials, and its boundless potential. This convergence of deep learning and precision agriculture heralds a new dawn for sustainable farming and global food security.

Keywords

Artificial Intelligence, Deep Learning, Precision Agriculture, Convolutional Neural Networks, Image Processing

Citations

IRE Journals:
Mansi Bapu Zanje , Sneha Balu Shirke , Ishwari Sanjay Yadav , Prof. B. B. Deshmukh "Review Paper: AI-Driven Plant Disease Diagnosis – A Deep Learning Approach in Precision Agriculture" Iconic Research And Engineering Journals Volume 8 Issue 12 2025 Page 241-245

IEEE:
Mansi Bapu Zanje , Sneha Balu Shirke , Ishwari Sanjay Yadav , Prof. B. B. Deshmukh "Review Paper: AI-Driven Plant Disease Diagnosis – A Deep Learning Approach in Precision Agriculture" Iconic Research And Engineering Journals, 8(12)